predict.enfa {adehabitat} | R Documentation |
predict.enfa
computes habitat suitability maps using the
Ecological-Niche Factor Analysis and the Mahalanobis distances
method.
## S3 method for class 'enfa': predict(object, index, attr, nf, ...)
object |
an object of class enfa |
index |
an integer vector giving the position of the rows of
tab in the initial object of class kasc . |
attr |
an object of class kasc or mapattr . |
nf |
the number of axes of specialization kept for the
predictions. By default, all axes kept in object are
used |
... |
further arguments passed to or from other methods |
The predictions are based on the position of the niche defined by the
ENFA within the multidimensional space of environmental variables. The
ENFA produces row coordinates for each pixel, which are used with the
function mahalanobis
. For each pixel, this function computes the
Mahalanobis distances from the barycentre of the niche.
Actually, the function predict.enfa
is identical to the function
mahasuhab
, except that the habitat suitability map is computed
using the axes of the ENFA, instead of the raw data.
Note that the MADIFA allows a more consistent factorial decomposition of the Mahalanobis distances.
Returns a raster map of class kasc
.
Mathieu Basille basille@biomserv.univ-lyon1.fr
Clark, J.D., Dunn, J.E. and Smith, K.G. (1993) A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management, 57, 519–526.
Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. (2002) Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? Ecology, 83, 2027–2036.
mahalanobis
for information on the computation of
Mahalanobis distances. mahasuhab
for more details on the
computation of habitat suitability maps using the Mahalanobis distances.
madifa
for a more consistent factorial decomposition of
the Mahalanobis distances
## Not run: data(lynxjura) map <- lynxjura$map ## We keep only "wild" indices. tmp <- lynxjura$loc[,4] != "D" locs <- lynxjura$locs[tmp, c("X","Y")] dataenfa1 <- data2enfa(map, locs) (enfa1 <- enfa(dudi.pca(dataenfa1$tab, scannf=FALSE), dataenfa1$pr, scannf = FALSE)) ## Compute the prediction pred <- predict(enfa1, dataenfa1$index, dataenfa1$attr) image(pred) contour(pred, col="green", add=T) points(locs, col = "red", pch = 16) ## Lighter areas are the most preferred areas ## End(Not run)